AI-102: Azure AI Engineer Associate

Name: AI-102: Azure AI Engineer Associate
Rating: 4.3 (15 reviews)

Azure AI Services, Azure OpenAI, Generative AI & Responsible AI Implementation

Created byMaruti Makwana

Last updated 2/2026

English

What you'll learn

Design and implement Azure AI solutions using Azure AI Services, Azure OpenAI, and Azure AI Foundry.
Build AI-powered applications using computer vision, natural language processing, and speech services.
Implement generative AI solutions using Azure OpenAI models with prompt engineering and orchestration.
Integrate Responsible AI practices including security, monitoring, and content safety in AI solutions.
Deploy, monitor, and manage AI solutions using Azure tools and best practices.

Course content

1 section • 8 lectures • 1h 57m total length

Plan and Prepare to Develop AI Solutions on Azure17:24
In this lecture, you’ll learn how to plan and prepare Azure environments for AI solutions, including choosing the right Azure AI services, understanding solution requirements, and aligning designs with AI-102 exam objectives.
Choose and Deploy Models from the Model Catalog11:06
In this lecture, you’ll learn how to select appropriate models from the Azure AI model catalog and deploy them for use in AI solutions. You’ll understand model selection criteria, deployment options, and how these steps are assessed in the AI-102 exam.
Lab – Choose, Deploy and Compare Language Models19:38
In this hands-on lab, you’ll choose, deploy, and compare language models from the Azure model catalog. You’ll evaluate model behavior, performance, and suitability for different AI solution scenarios relevant to the AI-102 exam.
Develop an AI app with the Azure AI Foundry SDK5:24
In this lecture, you’ll learn how to develop an AI application using the Azure AI Foundry SDK. You’ll explore how to integrate models, configure resources, and implement core AI functionality as part of an end-to-end Azure AI solution aligned with AI-102 objectives.
Lab - Develop an AI App with the Azure AI Foundry SDK16:25
In this practical lab, you’ll build an AI application using the Azure AI Foundry SDK. You’ll work through integrating models, configuring services, and testing the app flow, reinforcing skills required for the AI-102 exam.
Develop a RAG-based solution with your own data using Azure AI Foundry6:14
In this lecture, you’ll learn how to build a Retrieval-Augmented Generation (RAG) solution using your own data with Azure AI Foundry. You’ll understand data ingestion, retrieval, and grounding model responses—key skills tested in the AI-102 exam
Lab – RAG with Azure AI Foundry (Part 1)23:37
In this practical lab, you’ll start building a Retrieval-Augmented Generation (RAG) solution using Azure AI Foundry. You’ll focus on setting up data sources, indexing content, and preparing retrieval workflows, aligned with AI-102 implementation scenarios.
Lab – RAG with Azure AI Foundry (Part 2)17:43
In this lab, you’ll continue building the RAG solution by connecting retrieval results to generative models, grounding responses, and testing end-to-end behavior using Azure AI Foundry, as required for AI-102 scenarios.

Requirements

Basic knowledge of Microsoft Azure services and the Azure portal is recommended.
Familiarity with programming concepts using Python or C# will be helpful.
Understanding of REST APIs and JSON is beneficial for working with Azure AI services.
Prior exposure to AI or machine learning concepts
An Azure subscription is recommended for hands-on practice, though demos are provided in the course.

Description

The AI-102: Azure AI Engineer Associate certification validates your ability to build, integrate and manage AI solutions using Microsoft Azure.

This course is designed for developers and technical professionals who want hands-on experience implementing AI capabilities in real applications.

In this course, you’ll learn how to design and develop AI-powered solutions using Azure AI Services, Azure OpenAI and Azure AI Foundry. You’ll work with core AI workloads including computer vision, natural language processing, speech and generative AI and understand how these services are integrated into scalable, secure Azure architectures.

The course focuses on practical implementation, not just theory. You’ll explore how to call Azure AI services using APIs and SDKs, apply prompt engineering with Azure OpenAI models, and manage AI resources throughout their lifecycle. Special attention is given to Responsible AI, including security, monitoring, content safety, and compliance—key areas tested in the AI-102 exam.

You’ll also learn how to deploy, monitor and optimize AI solutions using Azure tools and best practices, helping you build solutions that are production-ready and aligned with enterprise requirements.

By the end of this course, you’ll have:

A strong understanding of how to implement Azure AI solutions end-to-end
Practical experience with Azure OpenAI and Azure AI Services
Confidence to attempt the AI-102 certification exam

Whether you’re preparing for the AI-102 exam or building real-world AI applications on Azure, this course gives you the technical depth and structure needed to succeed.

Who this course is for:

Azure developers and AI engineers preparing for the AI-102: Designing and Implementing a Microsoft Azure AI Solution exam
Software developers who want to integrate AI capabilities such as vision, speech, language, and generative AI into applications
Solution architects and cloud engineers involved in designing AI-enabled solutions on Azure
Professionals who have completed AI-900 and want to move to an implementation-focused AI role
Developers interested in Azure OpenAI, Azure AI Services, and Azure AI Foundry